class_docstr: Extension of Transresnet to incorporate dialogue history and multimodality.
docstr: Choose top k best responses for a single example.
  
  :param idx:
      idx of example in encoded
  :param encoded:
      full matrix of encoded representations (for the whole batch)
  :param candidates:
      list of candidates
  :param candidates_encoded:
      encoding of the candidates
  :param one_cand_set:
      true if there is one set of candidates for each example
  :param k:
      how many ranked responses to return
  
  :return:
      ranked list of k responses
docstr_tok: ["Choose","top","k","best","responses","for","a","single","example",".",":","param","idx",":","idx","of","example","in","encoded",":","param","encoded",":","full","matrix","of","encoded","representations","(","for","the","whole","batch",")",":","param","candidates",":","list","of","candidates",":","param","candidates_encoded",":","encoding","of","the","candidates",":","param","one_cand_set",":","true","if","there","is","one","set","of","candidates","for","each","example",":","param","k",":","how","many","ranked","responses","to","return",":","return",":","ranked","list","of","k","responses"]
code: def choose_topk(
      self,
      idx,
      encoded,
      candidates,
      candidates_encoded,
      one_cand_set,
      k,
  ):
      """
      Choose top k best responses for a single example.
  
      :param idx:
          idx of example in encoded
      :param encoded:
          full matrix of encoded representations (for the whole batch)
      :param candidates:
          list of candidates
      :param candidates_encoded:
          encoding of the candidates
      :param one_cand_set:
          true if there is one set of candidates for each example
      :param k:
          how many ranked responses to return
  
      :return:
          ranked list of k responses
      """
      encoding = encoded[idx : idx + 1, :]
      scores = torch.mm(
          candidates_encoded[idx]
          if not one_cand_set
          else candidates_encoded,
          encoding.transpose(0, 1),
      )
      if k >= 1:
          _, index_top = torch.topk(scores, k, dim=0)
      else:
          _, index_top = torch.topk(scores, scores.size(0), dim=0)
      return [
          candidates[idx][idx2]
          if not one_cand_set
          else candidates[idx2]
          for idx2 in index_top.unsqueeze(1)
      ]
  
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  import torch
  from torch import nn
  from parlai.utils.io import PathManager
  from parlai.agents.transformer.modules import TransformerEncoder, create_position_codes, TransformerEncoderLayer
  from projects.personality_captions.transresnet.modules import TransresnetModel, load_fasttext_embeddings
label_desc: Use image node_id 11 for calling the TransresnetMultimodalModel obj's underlying member method code with example usage: obj.choose_topk(idx, encoded, candidates, candidates_encoded, one_cand_set, k) and returns: unknown